Senior Staff Product Manager, AI Platform

ServiceNow•Santa Clara, CA

3d•Remote

About The Position

This is a rare seat for an agentic AI product leader: full end-to-end ownership of the inference layer that powers AI across an enterprise platform used by thousands of customers, not a single feature or model, but the infrastructure, economics, and vendor strategy behind all of them. You'll operate at the intersection of technology and business strategy, shaping how open-source and third-party models are selected, integrated, and optimized for cost efficiency across some of the most complex, regulated environments in the industry. ServiceNow's cross-vendor, multi-model approach and scale as a top enterprise AI adopter offer a front-row seat to how the AI inference landscape is evolving, with the influence to shape that strategy company-wide rather than execute someone else's roadmap.

Requirements

10+ years of product management experience, recent hands-on experience shipping agentic products.
Deep technical fluency in inference technology: model serving, GPU economics, and latency/throughput trade-offs.
Solid understanding of data sovereignty requirements and how they shape deployment decisions across regulated markets.
A track record of vendor partnerships and build-vs-buy decision-making at scale.
Demonstrated ability to operate independently on ambiguous, company-level problems.
Proven skill influencing senior stakeholders without formal authority.
Comfort working cross-vendor across a multi-model, multi-hyperscaler environment.

Nice To Haves

Prior experience partnering directly with hyperscaler platforms on AI infrastructure strategy.
Experience shipping AI products or platforms into regulated or highly compliance-sensitive customer environments.

Responsibilities

Strategy and roadmap for the AI inference layer end-to-end, across every region and deployment environment ServiceNow operates in.
Third-party and open-source model availability, selection, and integration — including performance, latency, and integration agility trade-offs.
Inference economics: shaping cost-per-token and unit economics as usage volume scales.
Vendor and partnership decisions behind the model layer, including build-vs-buy calls and hyperscaler relationships.
Model strategy and transitions across data center, hyperscaler, and regulated-environment deployments, including data sovereignty requirements.
Cross-functional execution alongside the existing AI Platform product team as the group scales to meet growing enterprise demand.
Enabling ServiceNow's AI strategy at scale — keeping model integration, cost optimization, and regional/regulatory rollout moving without becoming a bottleneck for the business or its customers, while continuously improving inference cost-efficiency as volume grows.